Artificial lift systems are widely used to enhance production for reservoirs with formation pressure too low to provide enough energy to directly lift fluids to the surface. Examples of artificial lift systems include gas lift systems, hydraulic pumping units, electric submersible pumps (ESPs), progressive cavity pumps (PCPs), plunger lift systems, and rod pump systems. Sucker rod pumps are currently the most commonly used artificial lift system in the industry.
Sucker rod pump failures can be broadly classified into two main categories: mechanical and chemical. Mechanical failures are typically caused by improper design, by improper manufacturing, or by wear and tear during operations. For example, well conditions such as sand intrusions, gas pounding, and asphalting can contribute to such wear and tear. Chemical failures are generally caused by the corrosive nature of the fluid being pumped through the systems. For example, the fluid may contain hydrogen sulfide (H2S) or bacteria. Typically these mechanical and chemical failures manifest as tubing failures, rod string failures and rod pump failures. These failures initially reduce the efficiency of the pumping operation and ultimately result in system failure, which shuts down the systems and requires reactive well workovers (as opposed to proactive maintenance). Such workovers cause production loss and an increase in Operational Expenditure (OPEX) beyond regular maintenance costs.
Currently pump off controllers (POCs) play a significant role in monitoring the operation of rod pump systems. POCs can be programmed to automatically shut down units if the values of torque and load deviate beyond a torque/load threshold. Also, the general behavior of rod pump systems can be understood by analyzing the dynamometer card patterns collected by the POCs. This helps reduce the amount of work required by the production and maintenance personnel operating in the field. However, the POCs by themselves are not sufficient as a great deal of time and effort is still needed to monitor each and every operating unit. Furthermore, the dataset obtained by POCs poses difficult challenges to data mining and machine learning applications with respect to high dimensionality, noise, and inadequate labeling.
The data collected from POCs is inherently highly dimensional, as POC controllers gather and record periodic artificial lift system measurements indicating production and artificial lift system operational statuses through load cells, motor sensors, pressure transducers and relays. For example, in a dataset having 14 attributes where each attribute is measured daily, the dimension for a single rod pump system is 1400 for a hundred day dataset. This highly dimensional data is problematic as it becomes increasingly difficult to manipulate, find matching patterns, and process the data to construct and apply models efficiently.
Datasets for artificial lift systems also tend to be very noisy. The noise, which can be natural or manmade, is often produced from multiple sources. For example, lightning strikes can sometimes disrupt wireless communication networks. Data collected by the POC sensors, therefore, might not be received by a centralized logging database, which results in missing values in the data. Additionally, artificial lift systems operate in rough physical environments that often leads to equipment break down. Petroleum engineering field workers regularly perform maintenance and make calibration adjustments to the equipment. These maintenance activities and adjustments can cause the sensor measurements to change—sometimes considerably. It is currently not standard practice to record such adjustments and recalibrations. Furthermore, while workers are generally diligent with regards to logging their work in downtime and workover database tables, occasionally a log entry is delayed or not logged at all. Another source of data noise is the variation caused by the force drive mechanisms. Lastly, in oil fields with insufficient formation pressure, injection wells are sometimes used to inject fluids (e.g., water, steam, carbon dioxide) to drive the oil toward the oil production wells. This injection can also affect the POC sensors measurements.
The dataset is also not explicitly labeled. Manually labeling the dataset is generally too time consuming and very tedious, especially considering access to petroleum engineering subject matter experts (SMEs) is often limited. Fully automatic labeling can also be problematic. For example, although the artificial lift system failure events are recorded in the artificial lift database, they are not suitable for direct use because of semantic differences in the interpretation of artificial lift system failure dates. The artificial lift system failure dates in the database do not correspond to the actual failure dates, or even to the dates when the SMEs first noticed the failures. Rather, the recorded failure dates typically correspond to the date when the workers shut down an artificial lift well to begin repairs. Because of the backlog of artificial lift system repair jobs, the difference can be several months between the actual failure dates and the recorded failure dates. Moreover, even if the exact failure dates are known, differentiation of the failures among normal, pre-failure and failure signals still needs to be performed.
FIG. 1 shows an example artificial lift system failure where several selected attributes collected through POC equipment are displayed. In particular, FIG. 1 illustrates peak surface load, surface card area, and the number of pumping cycles. As shown in FIG. 1, the failure of the artificial lift system was detected by field personnel on Mar. 31, 2010. After pulling all the pumping systems above the ground, it was discovered that there were holes on the tubing that caused leaking problems, which in turn, reduced the fluid load the rod pump carried to the surface. Through a “look back” process, subject matter experts determined “rod cut” events likely started as early as Nov. 25, 2009 where the rod began cutting the tubing. The problem grew worse over time, cutting large holes into the tubing. The actual leak likely started around Feb. 24, 2010. Therefore, the difference between the actual failure date and the recorded failure date was over a month.
There is a need for more automated systems, such as artificial intelligent systems that can dynamically keep track of certain parameters in each and every unit, give early indications or warnings of failures, and provide suggestions on types of maintenance work required based on the knowledge acquired from previous best practices. Such systems would be an asset to industry personnel by allowing them to be more proactive and to make better maintenance decisions. These systems would increase the efficiency of the pumping units and bring down Operating Expenditure (OPEX), thereby making pumping operations more economical.